1.
Research
Shorter Anogenital Distance Predicts Poorer Semen Quality in Young Men
in Rochester, New York
Jaime Mendiola,1,2 Richard W. Stahlhut,1 Niels Jørgensen,3 Fan Liu,1 and Shanna H. Swan1,4
1Department
of Obstetrics and Gynecology, School of Medicine and Dentistry, University of Rochester, Rochester, New York, USA;
of Preventive Medicine and Public Health, School of Medicine, University of Murcia, Murcia, Espinardo, Spain; 3University
Department of Growth and Reproduction, University of Copenhagen, Rigshospitalet, Copenhagen, Denmark; 4Mount Sinai School of
Medicine, New York, New York, USA
2Division
Background: In male rodents, anogenital distance (AGD) provides a sensitive and continuous
correlate of androgen exposure in the intra­ terine environment and predicts later reproductive sucu
cess. Some endocrine-disrupting chemicals can alter male reproductive tract development, including
shortening AGD, in both rodents and humans. Whether AGD is related to semen quality in human
is unknown.
Objective: We examined associations between AGD and semen parameters in adult males.
Methods: We used multiple regression analyses to model the relationships between sperm parameters and two alternative measures of AGD [from the anus to the posterior base of the scrotum
(AGDAS) and to the cephalad insertion of the penis (AGDAP)] in 126 volunteers in Rochester,
New York.
Results: AGDAS, but not AGDAP, was associated with sperm concentration, motility, morphology, total sperm count, and total motile count (p-values, 0.002–0.048). Men with AGDAS below
(vs. above) the median were 7.3 times more likely (95% confidence interval, 2.5–21.6) to have a low
sperm concentration (< 20 × 106/mL). For a typical study participant, sperm concentrations were
34.7 × 106/mL and 51.6 × 106/mL at the 25th and 75th percentiles of (adjusted) AGDAS.
Conclusions: In our population, AGDAS was a strong correlate of all semen parameters and a predictor of low sperm concentration. In animals, male AGD at birth reflects androgen levels during
the masculinization programming window and predicts adult AGD and reproductive function. Our
results suggest, therefore, that the androgenic environment during early fetal life exerts a fundamental influence on both AGD and adult sperm counts in humans, as demonstrated in rodents.
Key words: anogenital distance, anti­ ndrogens, endocrine disruption, semen quality, testicular
a
dysgenesis. Environ Health Perspect 119:958–963 (2011). doi:10.1289/ehp.1103421 [Online
4 March 2011]
A wide range of environmental chemicals
can interfere with androgen production and
signaling and have been shown to alter the
development of the male reproductive tract
in experimental animals (Foster 2006; Gray
et al. 2006). Establishing links between anti­
androgenic exposure in utero and similar outcomes in humans is challenging, however, in
part because the genital anomalies traditionally
examined in humans (e.g., hypospadias) occur
with such a low incidence that studying them
requires very large populations. Thus, a more
sensitive (and continuous) meas­ re of the
u
develop­ ental androgenic milieu is desirable.
m
Anogenital distance (AGD; distance from
anus to genitals) may serve as such a meas­ re.
u
AGD is routinely used in animal toxicology
studies and is the developmental end point
most sensitive to anti­ ndrogenic exposure. In
a
rodents and other mammals, AGD has been
shown to reflect the amount of androgen to
which a male fetus is exposed in early development; higher in utero androgen exposure
results in longer and more masculine AGD.
Recent interest in reproductive effects of anti­
androgens has focused on the phthalates,
particularly diethylhexyl phthalate (DEHP)
and dibutyl phthalate (DBP), whose anti­
androgenic effects have been directly demon­
958
strated in rodent models (Gray et al. 2006;
Scott et al. 2008). We previously reported
strong inverse associations between pre­ atal
n
phthalate exposure (particularly DEHP and
DBP) and shorter male AGD in human
infants (Swan 2008; Swan et al. 2005).
In many rodent studies, shortened AGD is
seen in conjunction with frank defects such as
hypospadias and cryptorchidism, and shorter
AGD has been seen in conjunction with
hypospadias in human males (Hsieh et al.
2008). Moreover, in male rodents, shortened
(weight-adjusted) AGD persists into adulthood (Hotchkiss et al. 2004) and predicts
compromised reproductive function in the
mature male (Macleod et al. 2010; Scott et al.
2008). However, to our knowledge, no study
has examined associations between AGD in
adults and sperm number or quality.
Definitively demonstrating that AGD
provides a link between prenatal anti­ ndrogen
a
exposure and adult reproductive function in
humans would require the availability of biological samples reflecting pre­ atal exposure
n
and subsequent follow-up across the many
years between exposure and sexual maturation. However, associations between AGD
and adult reproductive function in humans
would provide indirect evidence.
volume
In this study, we explored the hypothe­ is
s
that AGD may be a predictor of semen quality
in adult humans. If confirmed, this biomarker
may provide information about the androgenic
hormonal milieu during fetal development and
may be useful in studies of reproductive development and function in adulthood.
Materials and Methods
Study population. Subjects were participants in
the Rochester Young Men’s Study (RYMS), a
cross-sectional study of young men conducted
in 2009–2010 at the University of Rochester
(Rochester, NY). RYMS is part of an international study funded by the European Union
Seventh Framework Program (Environment),
“Developmental Effects of Environment on
Reproductive Health” (DEER). Men were
recruited into RYMS through flyers and newspapers at college and university campuses
in the Rochester area. Subjects were eligible
if they were born in the United States after
31 December 1987, able to read and speak
English, and able to contact their mother
and ask her to complete a questionnaire. In
response to advertisements placed at local colleges, a total of 389 potential participants contacted our study coordinator between spring
2009 and spring 2010. Of these, 305 (78.4%)
met all eligibility criteria, and 222 men participated in the study. AGD measurements
were obtained only for men who enrolled
September 2009 and later. One man with a
history of testicular cancer was azospermic
(sperm count of 0) and was not included in
analysis. Motility data were excluded for one
man whose time to semen analysis exceeded
30 min. The analysis reported here includes all
Address correspondence to S.H. Swan, Department
of Obstetrics and Gynecology, School of Medicine
and Dentistry, University of Rochester, 601
Elmwood Ave., Box 668, Rochester, NY 14642
USA. Telephone: (585) 275-9182. Fax: (585) 2762171. E-mail: shanna_swan@urmc.rochester.edu
We thank L. Kochman, J. Stevens, K. Brewer,
and R. Herko for their assistance in data collection;
S. Sathyanarayana for valuable editorial suggestions and
use of Figure 1; K. Edell and L. Parlett for data manage­
ment; and the young men for their participation.
This work was supported by the European Union
7th Framework Programme Theme (6) (Environment),
“Developmental Effects of Environment on
Reproductive Health” (DEER), grant 212844.
The authors declare they have no actual or potential
competing financial interests.
­
Received 7 January 2011; accepted 1 March 2011.
119 | number 7 | July 2011 • Environmental Health Perspectives

2.
Anogenital distance and semen quality
126 men with complete data on all study outcomes and covariates, including both meas­
ures of AGD, except sperm morphology data,
which was available for only 124 men.
The study included a physical examination; blood, urine, and semen samples; and
completion of a brief questionnaire. Subjects
received $75 upon completion of all study
components. Data from the mother’s questionnaire were not considered in this analysis.
The University of Rochester Research Subjects
Review Board approved the study, and written
informed consent was obtained from all subjects before their participation.
Semen collection and analysis. Men collected semen samples by masturbation at the
clinic and were asked to report the time of
their previous ejaculation. Although they were
asked to abstain from ejaculation for at least
48 hr before sample collection, they were not
excluded if they had not. Abstinence times
reported to be > 240 hr (n = 3) were truncated at 240 hr. Sample processing was initiated within 30 min of collection. Ejaculate
volumes were estimated by specimen weight,
assuming a semen density of 1.0 g/mL. Sperm
concentration was evaluated by hemo­ ytometer
c
(Improved Neubauer; Hauser Scientific Inc.,
Horsham, PA, USA). Two chambers of the
hemo­ ytometer were counted, and the averc
age was used in this analysis. Motility was
analyzed using World Health Organization
(WHO 1999) criteria; the percentage of all
sperm that were classified as forward motile
(“A + B,” where highly or moderately progressive sperm are scored as “A” and slow or sluggish progressive sperm are scored as “B”) were
used in all analyses and considered motile in
this analysis. We also calculated the total sperm
count (volume × sperm concentration) and the
total motile count (volume × sperm concentration × percent motile). Smears for morphology were made, air-dried, fixed, and shipped
to the University Department of Growth
and Reproduction at the Rigshospitalet
(Copenhagen, Denmark). The slides were
Papanicolaou stained and assessed using strict
criteria (Menkveld et al. 1990). To increase
consistency and comparability of methods over
the course of the study, six sets of duplicate
semen samples were sent during the study from
the University of Copenhagen’s Department of
Growth and Reproduction to the Andrology
Laboratory (University of Rochester),
which is Clinical Laboratory Improvement
Amendments certified.
Physical examination. A physical examination of each participant was performed, and
weight and height assessed, on the same day as
semen, urine, and blood sampling. The presence of varicocele or other abnormalities were
noted, and testicular size was estimated using
Prader’s orchidometer (Andrology Australia,
Clayton, Victoria, Australia).
In this study we measured two variants
of AGD: The first was measured from the
cephalad insertion of the penis to the center
of the anus (AGDAP; Figure 1, point 1 to
point 3), and the second was measured from
the posterior base (first fold) of the scrotum
to the center of the anus (AGDAS; Figure 1,
point 2 to point 3). Both were measured
using a stainless-steel digital caliper (VWR
International, LLC, West Chester, PA, USA)
and made while the man was in the lithotomy
position, with his thighs at a 45° angle to the
examination table. To improve precision, the
examiner made each of these measurements
twice, and the mean of the two measurements
(within-observer mean) was used as the estimate. (More detailed instructions for conducting this exam and an anatomically correct
figure demonstrating landmarks are available
upon request.)
A single examiner (J.M.) conducted most
of the exams (94%), and a second (J. Stevens)
examined the remaining seven men. Both
examiners independently examined eight
of these men in three sessions conducted
throughout the collection period. Neither the
examiners nor the support staff had knowledge of the men’s semen quality.
Statistical analyses. Sperm concentration,
total sperm count, and total motile count
were logarithmically transformed to normalize
their distributions. We examined possible drift
in measurements by including exam date in
multi­ ariate analyses both as a continuous and
v
as a categorical variable. We assessed withinobserver variability in the AGD measure­ ents
m
by calculating the mean absolute difference
in measurements. We used multiple regression analyses to identify predictors of each of
the two AGD measurements. We then determined the relative importance of these predictors by examining the partial correlations
between the measurement and the predictor,
controlling for other variables in the model.
We also used multiple regression analyses to
examine associations between AGD measurements and each semen parameter. Covariates
initially examined, both as predictors of AGD
measurements and as predictors of semen
parameters, were ethnicity, height, body mass
index (BMI), examiner, smoking status (current smoker vs. not current smoker), exam
date, testicular volume, and presence of testicular abnormalities (varico­ ele and hydro­
c
cele). We also initially included a variable
reflecting the number of stressful life events
(Dohrenwend et al. 1978), previously shown
to be significantly related to sperm count
and motility (Gollenberg et al. 2010). When
inclusion of a potential covariate resulted in
a change in the β‑coefficient of < 10%, the
variable was not retained in final models. The
exception was recruitment period, which was
retained even though it had little effect on the
Environmental Health Perspectives • volume 119 | number 7 | July 2011
regression coefficients for all sperm parameters.
In addition, abstinence time was entered into
all models predicting sperm concentration,
volume, total sperm count, and total motile
count, and time from sample collection to
sample analysis was included in models predicting sperm motility and total motile count,
because these variables are commonly controlled in andrology research. In addition, we
examined AGD in relation to the likelihood
that a man’s sperm concentration fell below
20 × 10 6/mL (WHO 1999) using logistic
regression and controlling for the same covariates. Final models are described in “Results.”
Level of statistical significance was set at 0.05.
Once models were determined, two analysts
(J.M. and F.L.) conducted these analyses independently using SAS (version 8; SAS Institute
Inc., Cary, NC, USA) and SPSS (version 18.0;
SPSS Inc., Chicago, IL, USA).
Results
The RYMS study population was quite homogeneous. Participants were 18–22 years of
age (median age, 19.4 years), predominantly
Caucasian (81%), non­ mokers (73%), with
s
a median BMI of 24.2. Median sperm concentration was 53.5 × 106/mL, and median
total sperm count was 157 × 106. Demographic
and reproductive parameters are summarized
in Table 1. Although this was a population of
apparently healthy young men, 24.6% of them
had a sperm count < 20 × 106/mL, a commonly
used cutoff for subfertility (WHO 1999).
The distributions of both AGD AS and
AGDAP were approximately normal (Figure 2).
AGDAS (mean, 51.3 mm; median, 51.7 mm)
was, on average, 40% as long as AGDAP (mean,
128 mm; median, 126 mm), and the SDs of
these two measures were similar (14.5 mm
1
AGDAP
2
AGDAS
3
Figure 1. Landmarks for two measurements
of AGD: AGD AP , from the cephalad insertion
of the penis to the center of the anus (point 1
to point 3); and AGD AS, from the posterior base
(first fold) of the scrotum to the center of the anus
(point 2 to point 3). Adapted with permission from
Sathyanarayana et al. (2010).
959

3.
Mendiola et al.
and 13.0 mm for AGDAS and AGDAP, respectively) (Table 1). As expected, AGDAS and
AGDAP were highly correlated [Pearson correlation (R) = 0.60, p < 0.0001].
Variability of AGD measurements.
Variation with period of examination. Here we
refer to fall 2009 as the first recruitment period
and spring 2010 as the second recruitment
period. We observed a small but significant
decrease in both AGDAS and AGDAP between
the first recruitment period (n = 44) and the
second (n = 82). Mean AGDAS was 56.6 mm
and 48.5 mm, and mean AGDAP was 132 mm
and 126 mm, for men recruited during period 1
and period 2, respectively. Mean age, BMI,
abstinence time, and all semen parameters were
similar in the two recruitment periods. Despite
these differences between time periods, we
saw no significant time trend within periods.
Although neither exam date nor recruitment
period was associated with any of the semen
parameters (all p‑values > 0.24), we retained
recruitment period in all final models.
Within- and between-examiner variability.
The mean (absolute) difference within examiners was 1.39 mm for AGDAS (2.7% of mean
AGDAS) and 2.62 mm for AGDAP (2.1% of
mean AGDAP). We used a mixed model to
estimate the inter­ lass correlations, which were
c
0.91 [95% confidence interval (CI), 0.79–0.97]
and 0.95 (95% CI, 0.89–0.98) for AGDAS and
AGDAP, respectively. We repeated all regression
Table 1. Characteristics of RYMS participants (n = 126).
Variable
Age (years)
BMI (kg/m2)
Abstinence time (hr)
Time to start semen analysis (min)
Testicular volume (mL)
AGD
AGDAS (mm)
AGDAP (mm)
Semen parameters
Seminal volume (mL)
Sperm concentration (106/mL)
Percent motile sperm (A + B)a
Percent normal morphology (strict)b
Total sperm count (106)
Total motile count (106)a
Ethnicity (%)
Caucasian
African-American
Other
Current smokers (%)
Varicocele present (%)
Mean ± SD, or percent
19.7 ± 1.0
24.6 ± 3.5
92.7 ± 78.3
14.0 ± 7.1
28.7 ± 4.9
Median (IQR)
19.4 (18.8–20.3)
24.2 (22.5–25.8)
70.7 (61.4–98.6)
10.0 (10.0–15.0)
26.9 (26.3–33.8)
51.3 ± 14.5
128 ± 13.0
51.7 (43.1–61.1)
126 (118–135)
3.3 ± 1.6
72.6 ± 66.5
57.4 ± 15.5
8.4 ± 4.6
241 ± 269
143 ± 155
3.1 (2.1–4.3)
53.5 (19.8–99.3)
60.3 (49.3–69.0)
8.5 (5.0–12.4)
157 (66.6–321)
98.7 (30.5–197)
81.0
5.6
13.4
27.0
11.9
Abbreviations: IQR, interquartile range.
aOne man with long time to analysis was excluded from motility analyses (n = 125). bTwo men with no morphology analysis were excluded (n = 124).
30
Percent of men
35
30
Percent of men
35
25
20
15
10
25
20
15
10
5
0
5
10
20
30
40
50
60
70
80
0
90
104
AGDAS(mm)
112
120
128
136
144
152
160
168
AGDAP(mm)
Figure 2. Frequency distributions of AGDAS (A) and AGDAP (B) in our study population.
Table 2. Predictors of AGDAS and AGDAP in multivariate models.
Variable
Height (cm)
BMI (kg/m2)
Periodb
Adjusted R 2
aR 2
AGDAS
R 2 for single
β-Coefficient p-Value
variable
0.76
< 0.0001
0.13
0.97
0.004
0.06
–7.70
0.002
0.06
0.23
Percent
R 2a
51.9%
22.3%
25.8%
AGDAP
R 2 for single Percent
β-Coefficient p-Value
variable
R 2a
0.59
< 0.0001
0.09
20.2%
2.18
< 0.0001
0.34
73.3%
–4.75
0.01
0.03
6.5%
0.45
for single variable divided by adjusted R 2 for full model. bPeriod of study (fall 2009 vs. spring 2010).
960
volume
analyses omitting the seven subjects measured
by J. Stevens, and results were unchanged.
Predictors of AGD AS and AGD AP. We
examined variables that predicted AGD AS
and AGD AP (Table 2). BMI, height, and
recruitment period (fall 2009 vs. spring 2010)
were significant predictors of both meas­ res.
u
Testicular volume, testicular abnormalities,
stress, and ethnicity were not significantly
related to AGD, so we did not retain them in
the final models.
The model fit was better for predicting
AGDAP than AGDAS (adjusted R2 = 0.45 and
0.23, respectively). BMI accounted for most
of the variability in AGDAP but little of the
variability for AGDAS, whereas height was
more influential for AGDAS.
AGD and other covariates in relation to
semen parameters. Covariates retained in final
models predicting semen parameters were
AGD meas­ res, height, recruitment period,
u
ethnicity (African American or not), abstinence time, and time to sample analysis as
described in “Materials and Methods.”
AGDAS was positively related to sperm
concentration, motility, morphology, total
sperm count, and total motile count (p‑values
0.002, 0.028, 0.048, 0.006, and 0.009, respectively; Table 3). The associations between
AGDAP and sperm count and concentration
were negligible, although in a similar direction
as those for AGDAS. Regression coefficients for
the two AGD meas­ res as predictors of sperm
u
motility and morphology were not inconsistent, although CIs for AGDAP were wider and
consistent with no association. The residual
plots for sperm concentration in relation to
AGDAS and AGDAP from our multi­ ariate
v
models, are shown in Figure 3.
We also examined sperm concentration
dichotomized at 20 × 106/mL (subfertile vs.
normal) in relation to AGD, controlling for
the same covariates used in the linear regression models. AGDAS was significantly related
to this outcome. The risk of subfertility was
increased 7.3 times (95% CI, 2.5–21.6) for
an (adjusted) AGDAS below the median, compared with AGDAS above the median. Having
a low sperm concentration (< 20 × 106/mL)
was inversely related to AGDAS (p < 0.0019).
AGDAP was not related to this outcome.
We calculated the expected change in
semen parameters associated with an interquartile increase in AGDAS for a typical study
participant. When AGDAS is 43.1 mm, the
25th percentile of the AGD distribution, the
expected sperm concentration, using our final
regression model, is 34.7 × 106/mL. When
AGDAS is 61.1 mm (the 75th percentile), the
expected sperm concentration is 51.6 × 106/
mL, whereas the predicted value for the 50th
percentile of AGDAS is 42.0 × 106/mL. Thus,
an interquartile increase in AGDAS is associated with an increase in sperm concentration
119 | number 7 | July 2011 • Environmental Health Perspectives

4.
Anogenital distance and semen quality
This is the first study to measure AGD in adult
men and examine the relationships between
AGD measures and sperm parameters. We
observed significant positive associations
between AGDAS and sperm concentration,
motility, morphology, total sperm count,
and total motile count. The associations we
observed between these sperm parameters
and AGD were stronger than those for most
covariates known to be associated with semen
quality. For example, the increase in sperm
concentration associated with an interquartile
increase in AGDAS is twice as large as that
expected in this population to be associated
with an interquartile increase in abstinence
time (8.4 × 106/mL), a known strong predictor of sperm concentration. Moreover, a
man with an AGDAS below the median was
7.3 times as likely to have a sperm concentration in the subfertile range (< 20 × 106/mL) as
a man with an AGDAS above the median. This
underscores the clinical implications of the
associations that we are reporting.
AGD measurements were well tolerated by
all subjects and quick to perform, with acceptable intra­ xaminer reliability. Unlike stee
roid hormones and semen parameters, AGD
measurements are not likely to be sensitive
to physiological and lifestyle factors (stress,
abstinence time, fever, smoking, etc.) and so
may need to be controlled only for body size,
as was the case in our study. Therefore, if our
results are confirmed, AGD may provide a
useful adjunct to these traditional measures of
male reproductive function.
Alternative measures of AGD. AGD has
long been measured in animal studies, but the
difference between AGDAS and AGDAP is not
readily apparent in newborn pups, although it
is clear in humans (Figure 1). AGD has only
recently been measured in epidemiological
studies, and methods for its reliable measurement are still being developed. Several
alternative measurements have been used
in examining AGD in human male infants.
Thankamony et al. (2009) and SalazarMartinez et al. (2004) used AGDAS, whereas
Sathyanarayana et al. (2010) ­ eas­ red
m u
index, including cube root of weight (Gallavan
et al. 1999) and weight at weaning (Hotchkiss
et al. 2004). Swan et al. (2005) used AGI
(AGD/weight at exam) at the mean age of
12.8 months, but this method did not completely remove the effect of weight. Therefore,
subsequent analysis (Swan 2008) used weight
percentile for age (see Centers for Disease
Control and Prevention 2010), a quantity that
is largely independent of weight and age and
that eliminates confounding by weight. Huang
et al. (2009) used AGD, AGD ÷ birth weight,
and AGD ÷ birth length. In RYMS men, we
found that BMI and height were both significantly associated with AGDAS and AGDAP,
and we included both measures in our models
predicting AGD.
In the present study, we examined men’s
sperm parameters in relation to two variants
of AGD: AGDAS and AGDAP. We saw significant associations with sperm parameters
only for AGDAS (Table 3, Figure 3). This may
in part be due to the strong influence of BMI
on adult AGDAP (Table 2), a quantity that
influences the size of the fat pad anterior to
the pubic symphysis, an area that is included
in AGDAP but not in AGDAS. It may also be,
however, that different AGD measurements
better reflect androgen exposures at different
life stages. Once a substantial body of normative data has been accumulated in infants and
Table 3. Multivariate analysis for men’s semen parameters and AGDAS and AGDAP.a
AGDAS
Semen parameter
β-Coefficient
95% CI
p-Value
Seminal volume (mL)
–0.002
–0.022 to 0.018 0.842
ln [sperm concentration
0.022
0.008 to 0.036 0.002*
(million/mL)]
Percent motile sperm (A + B)b,c
0.227
0.025 to 0.429 0.028*
Percent morphologically normal
0.061
0.0005 to 0.122 0.048*
spermd
ln [total sperm count (million)]
0.021
0.006 to 0.037 0.006*
ln [total motile sperm (million)]b,c
0.024
0.006 to 0.041 0.009*
AGDAP
β-Coefficient
95% CI
p-Value
–0.010
–0.032 to 0.011 0.343
0.008
–0.007 to 0.023 0.290
0.161
0.051
–0.055 to 0.379 0.142
–0.015 to 0.117 0.128
0.004
0.009
–0.012 to 0.021 0.596
–0.010 to 0.028 0.366
β-Coefficient indicates change in semen parameter associated with a 1 mm change in AGD.
aControlling for height, ethnicity (African American vs. not), period of study (fall 2009 vs. spring 2010), and ejaculation
abstinence time. bAlso controlling for time from semen collection to start of semen analysis. c n = 125; one man with long
time to analysis was excluded from motility analyses. dn = 124; two men without morphology were excluded. *p < 0.05.
500
500
250
250
Sperm concentration (million/mL)
Discussion
AGDAP. Romano-Riquer et al. (2007) used
a third measure (posterior base of the penis to
the anus), in addition to AGDAS and AGDAP.
Torres-Sanchez et al. (2008) introduced a
new measure (the distance from the tip of the
coccyx to the center of the anus).
In our previous analyses we measured
both AGDAS and AGDAP in human infants
and related these to phthalate metabolites in
maternal prenatal urine (Swan 2008; Swan
et al. 2005). We found inverse associations
that were, for most phthalate metabolites,
stronger with AGDAP than with AGDAS. The
distance covered by AGDAP is influenced by
penile width and scrotal size as well as AGDAS
(Figure 1). Penile width and testicular descent
were themselves inversely associated with
some phthalate concentrations in infants
(Swan 2008), as they are in rodents (Barlow
et al. 2004; Gray et al. 2006). Therefore, it is
possible that the associations between infant
AGD and phthalate metabolite concentrations
are stronger for AGDAP than for AGDAS.
Because AGD varies with body size, this
must be controlled in analysis. Methods for
doing this have varied. The anogenital index
(AGI; AGD divided by weight) was proposed
by Vandenbergh and Huggett (1995) as a
way to adjust AGD for body size in newborn
mice. Since then, various functions of weight
have been proposed in the calculation of this
Sperm concentration (million/mL)
that is 40.2% of the median, based on the
best-fitting model. Similar increases are
seen for other sperm parameters, although a
smaller increase is seen for percent morphologically normal sperm.
Height, BMI, and time period were all
associated with AGD and included in the
final models, although none of these variables was associated with any semen parameter in this population. All sperm parameters
were significantly lower in the small subgroup
(n = 7) of African-American men compared
with other men in this population (p‑values
for sperm parameters, < 0.001 to 0.016).
100
50
25
10
5
20
30
40
50
60
AGDAS(mm)
70
80
90
100
50
25
10
5
110
120
130
140
150
160
AGDAP(mm)
Figure 3. Partial regression plot (mean ± SE) of sperm concentration modeled as a function of (A) AGDAS
and (B) AGDAP.
Environmental Health Perspectives • volume 119 | number 7 | July 2011
961

5.
Mendiola et al.
adults, it should be possible to identify the
most androgen-sensitive measure (or meas­ res)
u
and determine which are most strongly related
to adult sexual function in adults. Until then,
we suggest that future studies continue to collect data on multiple measures.
Limitations. Our population was small
and limited in age and ethnicity and thus
cannot provide normative values for AGD
measure­ ents. We saw some differences in
m
AGD and semen quality by race, but numbers were too small to study this adequately.
Further, we obtained independent measurements by two examiners on only eight men,
too few to adequately estimate inter-rater reliability. Additionally, we noted a small but
systematic change in AGD measure­ ents
m
between fall 2009 and spring 2010. Although
semen parameters in this population did not
vary by study period and period did not confound our primary associations, these data
suggest possible measure­ ent drift and the
m
need for on­ oing quality control, including
g
frequent replicate measurements by independent examiners throughout the course of any
future study.
This is the first study in the United States
to report on semen quality in young, unselected men. We therefore cannot assess the representativeness of our study population. There
are, however, several studies in Europe that
evaluated semen quality in men at the time of
screening for military service (Jørgensen et al.
2002). Median sperm concentration in our
population was 53.5 × 106/mL, comparable
to that seen in young men in these European
countries (44–62 × 106/mL).
We measured testicular volume with a
Prader orchidometer. We saw significant associations between testicular volume and all
semen parameters except motility, but not
between testicular volume and either meas­
ure of AGD (data not shown). We could not
determine whether this is a result of the relatively coarse measure­ ents available with the
m
orchidometer or whether AGD is not correlated with testicular volume. The distribution of our testicular volume measurements
appeared to suggest a tendency to report volume in whole numbers (digit preference) and
was clearly not normally distributed. Possibly
an ultra­ ound measurement of testicular vols
ume would answer this question.
Our study participants provided only
a single semen sample. However, an earlier
study of semen quality in 697 men, most of
whom provided two samples, determined that
after adjusting for important covariates, it
made little difference in epidemiological studies whether the analysis includes men who
give one semen sample or two (Stokes-Riner
et al. 2007).
Finally, we plan to assess reproductive
hormones in a future study. A finding of
962
higher follicle-stimulating hormone and/or
low inhibin‑B or free testosterone in men with
shorter AGD would lend support to the association between AGD and semen variables we
report here.
Conclusions
Here we report data showing that one meas­
ure of AGD is strongly associated with multiple semen parameters, suggesting AGD’s
potential use as a biomarker of develop­ ental
m
anti­ ndrogen exposure. As animal studies
a
(Welsh et al. 2008) have clearly shown, AGD
is determined within a discrete masculinization programming window that is determined
by androgen action. Thus, the confirmation
we present here is highly plausible, because, to
date, all key relationships shown for AGD in
the rat have also been shown in humans.
If AGD (adjusted for body size) is determined prenatally in humans as in rodents, a
shorter AGD in adulthood should reflect a
shorter AGD at birth, which in turn reflects
decreased androgen exposure in utero. Thus,
both poorer semen quality and shorter AGD
in adulthood may reflect a common origin,
including a disruption of testicular development in utero, as suggested by the testicular dysgenesis syndrome (TDS) hypothesis
(Skakkebaek et al. 2001). As hypothesized,
this syndrome, although potentially multi­
factorial, may be caused by exposure to
endocrine-­ isrupting chemicals during the
d
masculinization programming window
(Scott et al. 2008). The increasing incidence
of male reproductive dis­ rders (Sharpe and
o
Skakkebaek 2008; Skakkebaek et al. 2001;
Toppari et al. 1996) and decreasing sperm
counts and testosterone levels (Andersson et al.
2007; Carlsen et al. 1992; Swan et al. 1997;
Travison et al. 2007) in many Western countries lend support to this hypothesis. Whether
shorter AGD in RYMS men reflects such dysgenesis and whether this is a consequence of
fetal anti­ ndrogen exposure are speculative.
a
However, the data we present here, together
with our prior study relating shorter AGD to
anti­ ndrogenic phthalate exposure in infants,
a
support that interpretation.
We suggest that a shortened male AGD
may be an important marker of human TDS.
An extended follow-up of a large cohort in
which AGD is measured in infancy would
be definitive, but logistically challenging.
However, confirmation in larger and more
diverse populations and in studies of AGD
in men with clinical manifestations of TDS
(infertile men, those born with cryptorchidism or hypospadias, or men with testicular
cancer) would provide persuasive evidence
that androgen action during early fetal life
exerts a fundamental influence on adult sperm
counts in humans, as has been demonstrated
in rodents.
volume
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119 | number 7 | July 2011 • Environmental Health Perspectives